A Comparative Study on Microaggregation Techniques for Microdata Protection

Microaggregation is an efficient Statistical Disclosure Control (SDC) perturbative technique for microdataprotection. It is a unified approach and naturally satisfies k-Anonymity without generalization orsuppression of data. Various microaggregation techniques: fixed-size and data-oriented for univariate andmultivariate data exists in the literature. These methods have been evaluated using the standard measures:Disclosure Risk (DR) and Information Loss (IL). Every time a new microaggregation technique wasproposed, a better trade-off between risk of disclosing data and data utility was achieved. Though thereexists an optimal univariate microaggregation method but unfortunately an optimal multivariatemicroaggregation method is an NP hard problem. Consequently, several heuristics have been proposed butno such method outperforms the other in all the possible criteria. In this paper we have performed a studyof the various microaggregation techniques so that we get a detailed insight on how to design an efficientmicroaggregation method which satisfies all the criteria